Molecular Learning with DNA Kernel Machines
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Noh, Yung-Kyun | - |
dc.contributor.author | Lee, Daniel D. | - |
dc.contributor.author | Yang, Kyung-Ae | - |
dc.contributor.author | Kim, Cheongtag | - |
dc.contributor.author | Zhang, Byoung-Tak | - |
dc.date.accessioned | 2022-07-16T00:51:08Z | - |
dc.date.available | 2022-07-16T00:51:08Z | - |
dc.date.created | 2021-05-13 | - |
dc.date.issued | 2015-01 | - |
dc.identifier.issn | 0303-2647 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/158052 | - |
dc.description.abstract | We present a computational learning method for bio-molecular classification. This method shows how to design biochemical operations both for learning and pattern classification. As opposed to prior work, our molecular algorithm learns generic classes considering the realization in vitro via a sequence of molecular biological operations on sets of DNA examples. Specifically, hybridization between DNA molecules is interpreted as computing the inner product between embedded vectors in a corresponding vector space, and our algorithm performs learning of a binary classifier in this vector space. We analyze the thermodynamic behavior of these learning algorithms, and show simulations on artificial and real datasets as well as demonstrate preliminary wet experimental results using gel electrophoresis. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER SCI LTD | - |
dc.title | Molecular Learning with DNA Kernel Machines | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Noh, Yung-Kyun | - |
dc.identifier.doi | 10.1016/j.biosystems.2015.06.007 | - |
dc.identifier.scopusid | 2-s2.0-84959471133 | - |
dc.identifier.wosid | 000365369800009 | - |
dc.identifier.bibliographicCitation | BIOSYSTEMS, v.137, pp.73 - 83 | - |
dc.relation.isPartOf | BIOSYSTEMS | - |
dc.citation.title | BIOSYSTEMS | - |
dc.citation.volume | 137 | - |
dc.citation.startPage | 73 | - |
dc.citation.endPage | 83 | - |
dc.type.rims | ART | - |
dc.type.docType | 정기학술지(Article(Perspective Article포함)) | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Life Sciences & Biomedicine - Other Topics | - |
dc.relation.journalResearchArea | Mathematical & Computational Biology | - |
dc.relation.journalWebOfScienceCategory | Biology | - |
dc.relation.journalWebOfScienceCategory | Mathematical & Computational Biology | - |
dc.subject.keywordPlus | DNA | - |
dc.subject.keywordAuthor | DNA computing | - |
dc.subject.keywordAuthor | Kernel methods | - |
dc.subject.keywordAuthor | Learning in vitro | - |
dc.subject.keywordAuthor | Machine learning | - |
dc.subject.keywordAuthor | Molecular algorithms | - |
dc.identifier.url | https://www.sciencedirect.com/science/article/pii/S0303264715000908?via%3Dihub | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea+82-2-2220-1365
COPYRIGHT © 2021 HANYANG UNIVERSITY.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.